Adversarial multi-source transfer learning in healthcare: Application to glucose prediction for diabetic people. (February 2021)

• Record Type: Journal Article
• Authors: De Bois, Maxime; El Yacoubi, Mounîm A.; Ammi, Mehdi
• Is Part Of: Computer methods and programs in biomedicine. Volume 199(2021)
• Publication Date: 2021-02
• Subjects: Artificial intelligence -- Deep learning -- Transfer learning -- Neural networks -- Diabetes -- Personalized medicine; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105874
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: In deep learning, multi-source transfer learning can alleviate the lack of data in healthcare. Deep models learn to discriminate data with different origins while training on multiple sources. Adversarial multi-source transfer learning incentivizes the learning of a more general feature representation. Personalized glucose predictive models in diabetes are significantly improved by first training them on multiple patients. Abstract: Background and objectives : Deep learning has yet to revolutionize general practices in healthcare, despite promising results for some specific tasks. This is partly due to data being in insufficient quantities hurting the training of the models. To address this issue, data from multiple health actors or patients could be combined by capitalizing on their heterogeneity through the use of transfer learning. Methods : To improve the quality of the transfer between multiple sources of data, we propose a multi-source adversarial transfer learning framework that enables the learning of a feature representation that is similar across the sources, and thus more general and more easily transferable. We apply this idea to glucose forecasting for diabetic people using a fully convolutional neural network. The evaluation is done by exploring various transfer scenarios with three datasets characterized by their high inter and intra variability. Results : While transferring knowledge is beneficial in general, we show that the statistical and clinical accuracies can be further improved by using of the adversarial training methodology, surpassing the current state-of-the-art results. In particular, it shines when using data from different datasets, or when there is too little data in an intra-dataset situation. To understand the behavior of the models, we analyze the learnt feature representations and propose a new metric in this regard. Contrary to a standard transfer, the adversarial transfer does not discriminate the patients and datasets, helping the learning of a more general feature representation. Conclusion : The adversarial training framework improves the learning of a general feature representation in a multi-source environment, enhancing the knowledge transfer to an unseen target. The proposed method can help improve the efficiency of data shared by different health actors in the training of deep models.